Transparent Reporting of Data Quality in Distributed Data Networks

Abstract

Introduction:Poor data quality can be a serious threat to the validity and generalizability of clinical research findings. The growing availability of electronic administrative and clinical data is accompanied by a growing concern about the quality of these data for observational research and other analytic purposes. Currently, there are no widely accepted guidelines for reporting quality results that would enable investigators and consumers to independently determine if a data source is fit for use to support analytic inferences and reliable evidence generation.Model and Methods:We developed a conceptual model that captures the flow of data from data originator across successive data stewards and finally to the data consumer. This “data lifecycle” model illustrates how data quality issues can result in data being returned back to previous data custodians. We highlight the potential risks of poor data quality on clinical practice and research results. Because of the need to ensure transparent reporting of a data quality issues, we created a unifying data-quality reporting framework and a complementary set of 20 data-quality reporting recommendations for studies that use observational clinical and administrative data for secondary data analysis. We obtained stakeholder input on the perceived value of each recommendation by soliciting public comments via two face-to-face meetings of informatics and comparative-effectiveness investigators, through multiple public webinars targeted to the health services research community, and with an open access online wiki.Recommendations:Our recommendations propose reporting on both general and analysis-specific data quality features. The goals of these recommendations are to improve the reporting of data quality measures for studies that use observational clinical and administrative data, to ensure transparency and consistency in computing data quality measures, and to facilitate best practices and trust in the new clinical discoveries based on secondary use of observational data.

Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0

“…If the data is periodically refreshed with more recent data, the date of the refresh should be reported as well as any changes in assumptions applied during the data transformation. 31,32 If cleaning decisions are made on a project specific basis rather than at a global data level, these should also be reported.…”

The Learning Healthcare System and Cardiovascular Care: A Scientific Statement From the American Heart Association

“…38 These include missing data (particularly data that directly inform clinical care), erroneous data, uninterpretable data, inconsistencies among providers and over time, and data stored in nonstructured text notes. 39 In addition, patients often receive care from multiple providers using different and poorly integrated EHR systems, thus making it difficult to completely track patients across practices or systems. Furthermore, critical clinical data are often recorded in unstructured, narrative text, complicating its use in LHS applications.…”

“…First, it would be ideal to create national data model and data storage standards to decrease the variability and to increase the utility of EHR data. 39,42 Second, EHR information exchange, which allows health systems to access and share EHR data across organizational and geographic boundaries, needs continued enhancement and dissemination to further increase the value of EHR data, particularly among patients who move among different healthcare systems. 43 National survey data from 2014 indicate that progress is being made, with 76% of hospitals reporting that they exchange health information with other hospitals, a 41% increase from 2008.…”

Methods for examining data quality in healthcare integrated data repositories

“…There is a lack of consensus on using these DQF for a comprehensive DQA of a given dataset as well as absence of a "one-framework-fits-all" solution for DQA. 16 In order to meet these requirements we developed a service-oriented architecture-based (SOA) DQA platform, Open Quality and Analytics Framework 17 (OQAF) consisting of three components: 1. Quality Knowledge Repository (QKR): We extracted DQC, their definitions and applicable measures, their relationships, and the computability of DQC in existing DQF (e.g.…”